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A Privacy-Preserving Subgraph-Level Federated Graph Neural Network via Differential Privacy

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 13370)

Abstract

Currently, the federated graph neural network (GNN) has attracted a lot of attention due to its wide applications in reality without violating the privacy regulations. Among all the privacy-preserving technologies, the differential privacy (DP) is the most promising one due to its effectiveness and light computational overhead. However, the DP-based federated GNN has not been well investigated, especially in the sub-graph-level setting, such as the scenario of recommendation system. The biggest challenge is how to guarantee the privacy and solve the non independent and identically distributed (non-IID) data in federated GNN simultaneously. In this paper, we propose DP-FedRec, a DP-based federated GNN to fill the gap. Private Set Intersection (PSI) is leveraged to extend the local graph for each client, and thus solve the non-IID problem. Most importantly, DP is applied not only on the weights but also on the edges of the intersection graph from PSI to fully protect the privacy of clients. The evaluation demonstrates DP-FedRec achieves better performance with the graph extension and DP only introduces little computations overhead.

Keywords

  • Recommendation system
  • Federated learning
  • Subgraph-level federated learning
  • Graph neural network
  • Differential privacy

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Acknowledgement

This paper is supported by the Key Research and Development Program of Guangdong Province under grant No. 2021B0101400003.

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Correspondence to Jianzong Wang .

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Qiu, Y., Huang, C., Wang, J., Huang, Z., Xiao, J. (2022). A Privacy-Preserving Subgraph-Level Federated Graph Neural Network via Differential Privacy. In: Memmi, G., Yang, B., Kong, L., Zhang, T., Qiu, M. (eds) Knowledge Science, Engineering and Management. KSEM 2022. Lecture Notes in Computer Science(), vol 13370. Springer, Cham. https://doi.org/10.1007/978-3-031-10989-8_14

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  • DOI: https://doi.org/10.1007/978-3-031-10989-8_14

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